DShrimp/PoseMaker
1
1import numpy as np2import math3import cv24import matplotlib5from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas6from matplotlib.figure import Figure7import numpy as np8import matplotlib.pyplot as plt9import cv210 11 12def padRightDownCorner(img, stride, padValue):13 h = img.shape[0]14 w = img.shape[1]15 16 pad = 4 * [None]17 pad[0] = 0 # up18 pad[1] = 0 # left19 pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down20 pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right21 22 img_padded = img23 pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))24 img_padded = np.concatenate((pad_up, img_padded), axis=0)25 pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))26 img_padded = np.concatenate((pad_left, img_padded), axis=1)27 pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))28 img_padded = np.concatenate((img_padded, pad_down), axis=0)29 pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))30 img_padded = np.concatenate((img_padded, pad_right), axis=1)31 32 return img_padded, pad33 34# transfer caffe model to pytorch which will match the layer name35def transfer(model, model_weights):36 transfered_model_weights = {}37 for weights_name in model.state_dict().keys():38 transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]39 return transfered_model_weights40 41# draw the body keypoint and lims42def draw_bodypose(canvas, candidate, subset):43 stickwidth = 444 limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \45 [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \46 [1, 16], [16, 18], [3, 17], [6, 18]]47 48 colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \49 [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \50 [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]51 for i in range(18):52 for n in range(len(subset)):53 index = int(subset[n][i])54 if index == -1:55 continue56 x, y = candidate[index][0:2]57 cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)58 for i in range(17):59 for n in range(len(subset)):60 index = subset[n][np.array(limbSeq[i]) - 1]61 if -1 in index:62 continue63 cur_canvas = canvas.copy()64 Y = candidate[index.astype(int), 0]65 X = candidate[index.astype(int), 1]66 mX = np.mean(X)67 mY = np.mean(Y)68 length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.569 angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))70 polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)71 cv2.fillConvexPoly(cur_canvas, polygon, colors[i])72 canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)73 # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])74 # plt.imshow(canvas[:, :, [2, 1, 0]])75 return canvas76 77def draw_handpose(canvas, all_hand_peaks, show_number=False):78 edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \79 [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]80 fig = Figure(figsize=plt.figaspect(canvas))81 82 fig.subplots_adjust(0, 0, 1, 1)83 fig.subplots_adjust(bottom=0, top=1, left=0, right=1)84 bg = FigureCanvas(fig)85 ax = fig.subplots()86 ax.axis('off')87 ax.imshow(canvas)88 89 width, height = ax.figure.get_size_inches() * ax.figure.get_dpi()90 91 for peaks in all_hand_peaks:92 for ie, e in enumerate(edges):93 if np.sum(np.all(peaks[e], axis=1)==0)==0:94 x1, y1 = peaks[e[0]]95 x2, y2 = peaks[e[1]]96 ax.plot([x1, x2], [y1, y2], color=matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0]))97 98 for i, keyponit in enumerate(peaks):99 x, y = keyponit100 ax.plot(x, y, 'r.')101 if show_number:102 ax.text(x, y, str(i))103 bg.draw()104 canvas = np.fromstring(bg.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)105 return canvas106 107# image drawed by opencv is not good.108def draw_handpose_by_opencv(canvas, peaks, show_number=False):109 edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \110 [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]111 # cv2.rectangle(canvas, (x, y), (x+w, y+w), (0, 255, 0), 2, lineType=cv2.LINE_AA)112 # cv2.putText(canvas, 'left' if is_left else 'right', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)113 for ie, e in enumerate(edges):114 if np.sum(np.all(peaks[e], axis=1)==0)==0:115 x1, y1 = peaks[e[0]]116 x2, y2 = peaks[e[1]]117 cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2)118 119 for i, keyponit in enumerate(peaks):120 x, y = keyponit121 cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)122 if show_number:123 cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)124 return canvas125 126# detect hand according to body pose keypoints127# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp128def handDetect(candidate, subset, oriImg):129 # right hand: wrist 4, elbow 3, shoulder 2130 # left hand: wrist 7, elbow 6, shoulder 5131 ratioWristElbow = 0.33132 detect_result = []133 image_height, image_width = oriImg.shape[0:2]134 for person in subset.astype(int):135 # if any of three not detected136 has_left = np.sum(person[[5, 6, 7]] == -1) == 0137 has_right = np.sum(person[[2, 3, 4]] == -1) == 0138 if not (has_left or has_right):139 continue140 hands = []141 #left hand142 if has_left:143 left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]144 x1, y1 = candidate[left_shoulder_index][:2]145 x2, y2 = candidate[left_elbow_index][:2]146 x3, y3 = candidate[left_wrist_index][:2]147 hands.append([x1, y1, x2, y2, x3, y3, True])148 # right hand149 if has_right:150 right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]151 x1, y1 = candidate[right_shoulder_index][:2]152 x2, y2 = candidate[right_elbow_index][:2]153 x3, y3 = candidate[right_wrist_index][:2]154 hands.append([x1, y1, x2, y2, x3, y3, False])155 156 for x1, y1, x2, y2, x3, y3, is_left in hands:157 # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox158 # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);159 # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);160 # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);161 # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);162 # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);163 x = x3 + ratioWristElbow * (x3 - x2)164 y = y3 + ratioWristElbow * (y3 - y2)165 distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)166 distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)167 width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)168 # x-y refers to the center --> offset to topLeft point169 # handRectangle.x -= handRectangle.width / 2.f;170 # handRectangle.y -= handRectangle.height / 2.f;171 x -= width / 2172 y -= width / 2 # width = height173 # overflow the image174 if x < 0: x = 0175 if y < 0: y = 0176 width1 = width177 width2 = width178 if x + width > image_width: width1 = image_width - x179 if y + width > image_height: width2 = image_height - y180 width = min(width1, width2)181 # the max hand box value is 20 pixels182 if width >= 20:183 detect_result.append([int(x), int(y), int(width), is_left])184 185 '''186 return value: [[x, y, w, True if left hand else False]].187 width=height since the network require squared input.188 x, y is the coordinate of top left 189 '''190 return detect_result191 192# get max index of 2d array193def npmax(array):194 arrayindex = array.argmax(1)195 arrayvalue = array.max(1)196 i = arrayvalue.argmax()197 j = arrayindex[i]198 return i, j199